Abstract
Currently, photonic mixing device (PMD) cameras undergo a great deal of attention. They allow simultaneous recordings of amplitude and distance images with one shot. This opens up new application possibilities like drivers’ assistance in vehicles or gesture control in the multimedia sector. Unfortunately, PMD cameras reach only low spatial resolution. Wherein the pixel resolution for state-of-the-art indoor cameras ranging about VGA resolution, they are even lower for outdoor applications. This limits the possibilities for object recognition. From two-dimensional (2D) imaging there are already methods known for increasing spatial resolution virtually. It means resolution enhancement without changing physically given sensor specifications like pixel dimension or sensor size. In this context, often referred as superresolution (SR). This work compares four well-known geometric SR algorithms from 2D imaging adapted to PMD imaging. Resolution enhancement and quality of the SR results are evaluated objectively by measuring the spatial frequency response (SFR) and investigating the noise performance in amplitude and distance images. Based on these results, SR algorithms for possible measurement tasks in metrological or photographic applications are proposed.
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